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EnglishThis paper presents a comparative study of two important Clonal Selection Algorithms (CSAs): CLONALG and opt-IA. To deeply understand the performance of both algorithms, we deal with four different classes of problems: toy problems (one-counting and trap functions), pattern recognition, numerical optimization problems and NP-complete problem (the 2D HP model for protein structure prediction problem). Two possible versions of CLONALG have been implemented and tested. The experimental results show a global better performance of opt-IA with respect to CLONALG. Considering the results obtained, we can claim that CSAs represent a new class of Evolutionary Algorithms for effectively performing searching, learning and optimization tasks.
| Titolo: | Clonal Selection Algorithms: A Comparative Case Study using Effective Mutation Potentials |
| Autori interni: | |
| Data di pubblicazione: | 2005 |
| Rivista: | |
| Handle: | http://hdl.handle.net/20.500.11769/71318 |
| ISBN: | 978-3-540-28175-7 |
| Appare nelle tipologie: | 4.1 Contributo in Atti di convegno |
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http://hdl.handle.net/20.500.11769/71318
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